Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Research Postgraduate



f.laumann18 Website




603Weeks BuildingSouth Kensington Campus





I am a PhD student at the Department of Mathematics, investigating the causal interlinkages amongst the United Nations' Sustainable Development Goals (SDGs). I view the 17 SDGs with their 169 targets as a network of 17 or 169 nodes, respectively, and try to find directed causal edges between them. Data for the indicators of the SDGs is publicly available here and I would like to see many more data scientists working with it.

My research interests lie in kernel methods, causal discovery and network theory, and I find it interesting to use them to learn more about the connections between macro-economics, societies, and natural environments — which eventually underpin the urge to govern sustainably.

I am supervised by Mauricio Barahona and work closely together with Julius von Kügelgen.

I obtained a MSc in Engineering from the Technical University of Denmark (DTU) and Aarhus University with a focus on (Bayesian) machine learning.


Laumann F, von Kügelgen J, Barahona M, 2020, Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals. ICLR workshop on Tackling Climate Change with Machine Learning.  arXiv preprint arXiv:2004.09318

Shridhar K, Laumann F, Liwicki M, 2019, Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference. arXiv preprint arXiv:1806.05978

Laumann F, Tambo T, 2018, Enterprise Architecture for a Facilitated Transformation from a Linear to a Circular Economy. Sustainability, 10(11), p.3882



Laumann F, von Kuegelgen J, Barahona M, 2021, Kernel two-sample and independence tests for non-stationary random processes, ITISE 2021 (7th International conference on Time Series and Forecasting),, Pages:1-13

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